1st Plot Attempt
Siddharth Namachivayam
2/6/2019
Plot of Albuquerque Data
Here is my plot of Albuquerque maximum temperatures overlayed with a best fit line:
Lets look at some of the results for this regression:
##
## Call:
## lm(formula = TMAX ~ DATES, data = climate_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.689 -8.106 0.825 8.728 20.295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.128e+01 5.608e-02 379.530 <2e-16 ***
## DATES 1.356e-05 5.931e-06 2.287 0.0222 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.85 on 32105 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0001629, Adjusted R-squared: 0.0001317
## F-statistic: 5.23 on 1 and 32105 DF, p-value: 0.02221Note that the p-value for the y-intercept is 2x10^-6 and the p-value for the slope of the line is .02. Both of these values are less than .05. In other words, we can say with 95% confidence that our temperature is increasing at a rate of 1.356x10^-5 degrees C per year and has an intercept of 21.28 degrees C.
Let’s go ahead and clean up our original graph by looking at monthly averages one month at a time.
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9898 -1.4768 -0.0783 1.8627 4.2359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -28.60050 16.91257 -1.691 0.0944 .
## YEAR 0.01882 0.00856 2.198 0.0306 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.04 on 86 degrees of freedom
## Multiple R-squared: 0.05319, Adjusted R-squared: 0.04218
## F-statistic: 4.831 on 1 and 86 DF, p-value: 0.03063
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.3104 -1.4311 0.0408 1.6153 4.7256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -25.027639 18.641886 -1.343 0.1830
## YEAR 0.018737 0.009438 1.985 0.0503 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.211 on 85 degrees of freedom
## Multiple R-squared: 0.04431, Adjusted R-squared: 0.03307
## F-statistic: 3.941 on 1 and 85 DF, p-value: 0.05034
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0155 -1.3019 -0.1369 1.1707 5.4288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -41.103197 15.697489 -2.618 0.010437 *
## YEAR 0.029099 0.007949 3.661 0.000434 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.894 on 86 degrees of freedom
## Multiple R-squared: 0.1348, Adjusted R-squared: 0.1247
## F-statistic: 13.4 on 1 and 86 DF, p-value: 0.0004336
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1130 -0.8838 -0.2057 1.1431 3.9821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.495014 14.479181 0.449 0.655
## YEAR 0.007533 0.007332 1.027 0.307
##
## Residual standard error: 1.747 on 86 degrees of freedom
## Multiple R-squared: 0.01212, Adjusted R-squared: 0.0006364
## F-statistic: 1.055 on 1 and 86 DF, p-value: 0.3071
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4346 -1.1179 -0.1707 0.9689 3.9850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.116514 13.179305 0.919 0.360
## YEAR 0.007294 0.006674 1.093 0.277
##
## Residual standard error: 1.59 on 86 degrees of freedom
## Multiple R-squared: 0.0137, Adjusted R-squared: 0.002231
## F-statistic: 1.195 on 1 and 86 DF, p-value: 0.2775
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1216 -0.8659 -0.0342 0.7996 3.4784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.720334 11.253071 -0.242 0.80956
## YEAR 0.017713 0.005699 3.108 0.00255 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 86 degrees of freedom
## Multiple R-squared: 0.101, Adjusted R-squared: 0.09054
## F-statistic: 9.661 on 1 and 86 DF, p-value: 0.002551
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3136 -0.8437 -0.1187 0.6718 3.9363
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.812784 10.078899 3.752 0.000318 ***
## YEAR -0.002250 0.005104 -0.441 0.660412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.216 on 86 degrees of freedom
## Multiple R-squared: 0.002255, Adjusted R-squared: -0.009347
## F-statistic: 0.1944 on 1 and 86 DF, p-value: 0.6604
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1766 -0.7669 -0.0452 0.8890 2.7807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.528318 10.006676 4.550 1.75e-05 ***
## YEAR -0.006950 0.005068 -1.372 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.208 on 86 degrees of freedom
## Multiple R-squared: 0.0214, Adjusted R-squared: 0.01003
## F-statistic: 1.881 on 1 and 86 DF, p-value: 0.1738
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7412 -1.1391 0.0648 1.0468 3.5288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.616378 11.784895 2.768 0.00691 **
## YEAR -0.002266 0.005968 -0.380 0.70514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.422 on 86 degrees of freedom
## Multiple R-squared: 0.001673, Adjusted R-squared: -0.009935
## F-statistic: 0.1441 on 1 and 86 DF, p-value: 0.7051
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3097 -1.3341 0.0656 1.2140 5.0281
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.293470 14.869752 2.643 0.00978 **
## YEAR -0.008848 0.007530 -1.175 0.24323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.794 on 86 degrees of freedom
## Multiple R-squared: 0.0158, Adjusted R-squared: 0.004356
## F-statistic: 1.381 on 1 and 86 DF, p-value: 0.2432
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8360 -1.2418 0.1803 1.1735 4.8920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.320367 15.829233 0.21 0.834
## YEAR 0.005449 0.008016 0.68 0.498
##
## Residual standard error: 1.91 on 86 degrees of freedom
## Multiple R-squared: 0.005345, Adjusted R-squared: -0.006221
## F-statistic: 0.4621 on 1 and 86 DF, p-value: 0.4985
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8498 -1.1722 -0.1227 1.3387 4.2202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.554731 16.036793 1.157 0.250
## YEAR -0.004923 0.008121 -0.606 0.546
##
## Residual standard error: 1.935 on 86 degrees of freedom
## Multiple R-squared: 0.004255, Adjusted R-squared: -0.007323
## F-statistic: 0.3675 on 1 and 86 DF, p-value: 0.546Ok cool. Let’s do the same thing except for TMIN data now instead.
Lets look at some of the results for this regression:
##
## Call:
## lm(formula = TMIN ~ DATES, data = climate_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.630 -7.494 -0.223 8.325 18.334
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.404e+00 5.044e-02 126.97 <2e-16 ***
## DATES 7.037e-05 5.334e-06 13.19 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.859 on 32105 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.005392, Adjusted R-squared: 0.005361
## F-statistic: 174 on 1 and 32105 DF, p-value: < 2.2e-16[SOME ANALYSIS] Let’s go ahead and clean up our original graph by looking at monthly averages one month at a time.
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.121 -1.257 -0.108 1.349 4.224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -71.850088 15.977413 -4.497 2.14e-05 ***
## YEAR 0.034087 0.008087 4.215 6.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.927 on 86 degrees of freedom
## Multiple R-squared: 0.1712, Adjusted R-squared: 0.1616
## F-statistic: 17.77 on 1 and 86 DF, p-value: 6.145e-05
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1934 -1.0198 -0.0768 0.9996 5.6681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -66.064772 16.069852 -4.111 9.05e-05 ***
## YEAR 0.032296 0.008136 3.970 0.00015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.906 on 85 degrees of freedom
## Multiple R-squared: 0.1564, Adjusted R-squared: 0.1465
## F-statistic: 15.76 on 1 and 85 DF, p-value: 0.00015
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3844 -0.8691 0.1963 0.9490 3.0536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -75.898882 11.538049 -6.578 3.55e-09 ***
## YEAR 0.038882 0.005843 6.654 2.52e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 86 degrees of freedom
## Multiple R-squared: 0.3399, Adjusted R-squared: 0.3322
## F-statistic: 44.28 on 1 and 86 DF, p-value: 2.525e-09
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1211 -0.7839 0.0959 0.9470 4.6590
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -43.754036 13.297688 -3.290 0.001452 **
## YEAR 0.024765 0.006734 3.678 0.000409 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.605 on 86 degrees of freedom
## Multiple R-squared: 0.1359, Adjusted R-squared: 0.1258
## F-statistic: 13.52 on 1 and 86 DF, p-value: 0.0004093
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6786 -0.9287 0.1068 1.1252 2.4969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -33.786160 12.238223 -2.761 0.007049 **
## YEAR 0.022297 0.006198 3.598 0.000535 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.477 on 86 degrees of freedom
## Multiple R-squared: 0.1308, Adjusted R-squared: 0.1207
## F-statistic: 12.94 on 1 and 86 DF, p-value: 0.0005354
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1994 -1.0735 -0.1924 0.8488 3.2773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -51.274069 11.224752 -4.568 1.63e-05 ***
## YEAR 0.033846 0.005684 5.954 5.54e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 86 degrees of freedom
## Multiple R-squared: 0.2919, Adjusted R-squared: 0.2837
## F-statistic: 35.45 on 1 and 86 DF, p-value: 5.541e-08
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.25029 -0.68049 0.01608 0.53894 2.62266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.80398 7.60250 -3.263 0.00158 **
## YEAR 0.02190 0.00385 5.687 1.74e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9174 on 86 degrees of freedom
## Multiple R-squared: 0.2733, Adjusted R-squared: 0.2648
## F-statistic: 32.34 on 1 and 86 DF, p-value: 1.743e-07
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.09133 -0.63185 0.03808 0.53974 2.11021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.158922 7.409235 -2.316 0.0229 *
## YEAR 0.017570 0.003752 4.683 1.05e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8941 on 86 degrees of freedom
## Multiple R-squared: 0.2032, Adjusted R-squared: 0.1939
## F-statistic: 21.93 on 1 and 86 DF, p-value: 1.049e-05
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3999 -0.8945 0.1578 0.9943 2.4423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -26.352264 10.398567 -2.534 0.013081 *
## YEAR 0.020289 0.005266 3.853 0.000224 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.255 on 86 degrees of freedom
## Multiple R-squared: 0.1472, Adjusted R-squared: 0.1373
## F-statistic: 14.84 on 1 and 86 DF, p-value: 0.0002244
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4268 -0.8014 0.0885 1.0423 4.0343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -35.222087 11.800345 -2.985 0.003694 **
## YEAR 0.021392 0.005976 3.580 0.000569 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 86 degrees of freedom
## Multiple R-squared: 0.1297, Adjusted R-squared: 0.1196
## F-statistic: 12.81 on 1 and 86 DF, p-value: 0.0005686
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1755 -0.6861 0.0688 0.7318 2.9373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -73.634316 12.187575 -6.042 3.79e-08 ***
## YEAR 0.037306 0.006172 6.044 3.74e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.471 on 86 degrees of freedom
## Multiple R-squared: 0.2982, Adjusted R-squared: 0.29
## F-statistic: 36.54 on 1 and 86 DF, p-value: 3.744e-08
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8536 -1.0239 0.1069 0.8080 3.3587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -40.380523 12.413131 -3.253 0.00163 **
## YEAR 0.018462 0.006286 2.937 0.00425 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.498 on 86 degrees of freedom
## Multiple R-squared: 0.09116, Adjusted R-squared: 0.08059
## F-statistic: 8.626 on 1 and 86 DF, p-value: 0.004252Ok lets take a break from all this temprature stuff and look at precipitation instead.
##
## Call:
## lm(formula = PRCP ~ DATES, data = climate_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.628 -0.614 -0.600 -0.586 48.205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.021e-01 1.420e-02 42.410 <2e-16 ***
## DATES 1.451e-06 1.502e-06 0.966 0.334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.494 on 32105 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 2.907e-05, Adjusted R-squared: -2.077e-06
## F-statistic: 0.9333 on 1 and 32105 DF, p-value: 0.334[SOME ANALYSIS] Let’s go ahead and clean up our original graph by looking at monthly averages one month at a time.
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32870 -0.22164 -0.07728 0.15257 0.80618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2588363 2.3338247 -0.111 0.912
## YEAR 0.0002917 0.0011813 0.247 0.806
##
## Residual standard error: 0.2815 on 86 degrees of freedom
## Multiple R-squared: 0.0007087, Adjusted R-squared: -0.01091
## F-statistic: 0.06099 on 1 and 86 DF, p-value: 0.8055
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41037 -0.21722 -0.07784 0.13403 1.26236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.444710 2.795373 -0.875 0.384
## YEAR 0.001421 0.001415 1.004 0.318
##
## Residual standard error: 0.3315 on 85 degrees of freedom
## Multiple R-squared: 0.01172, Adjusted R-squared: 9.686e-05
## F-statistic: 1.008 on 1 and 85 DF, p-value: 0.3182
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.40001 -0.27495 -0.05395 0.12151 1.52118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1674930 3.1514270 -0.053 0.958
## YEAR 0.0002815 0.0015959 0.176 0.860
##
## Residual standard error: 0.3803 on 86 degrees of freedom
## Multiple R-squared: 0.0003616, Adjusted R-squared: -0.01126
## F-statistic: 0.03111 on 1 and 86 DF, p-value: 0.8604
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4430 -0.3746 -0.1303 0.1628 2.1182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.140537 4.114377 0.277 0.782
## YEAR -0.000357 0.002084 -0.171 0.864
##
## Residual standard error: 0.4965 on 86 degrees of freedom
## Multiple R-squared: 0.0003412, Adjusted R-squared: -0.01128
## F-statistic: 0.02935 on 1 and 86 DF, p-value: 0.8644
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5895 -0.3430 -0.1631 0.1220 1.9195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.174908 4.381272 1.866 0.0655 .
## YEAR -0.003906 0.002219 -1.760 0.0819 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5287 on 86 degrees of freedom
## Multiple R-squared: 0.03478, Adjusted R-squared: 0.02356
## F-statistic: 3.099 on 1 and 86 DF, p-value: 0.08189
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6167 -0.3914 -0.1864 0.2100 2.5908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.730782 4.721300 1.214 0.228
## YEAR -0.002637 0.002391 -1.103 0.273
##
## Residual standard error: 0.5697 on 86 degrees of freedom
## Multiple R-squared: 0.01395, Adjusted R-squared: 0.002487
## F-statistic: 1.217 on 1 and 86 DF, p-value: 0.2731
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.12039 -0.54223 -0.09456 0.46470 1.60949
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.596604 5.663067 -1.695 0.0938 .
## YEAR 0.005447 0.002868 1.899 0.0609 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6834 on 86 degrees of freedom
## Multiple R-squared: 0.04026, Adjusted R-squared: 0.0291
## F-statistic: 3.607 on 1 and 86 DF, p-value: 0.06088
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2007 -0.5919 -0.1190 0.5910 1.8927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4929329 6.3526572 0.392 0.696
## YEAR -0.0006586 0.0032171 -0.205 0.838
##
## Residual standard error: 0.7666 on 86 degrees of freedom
## Multiple R-squared: 0.0004871, Adjusted R-squared: -0.01114
## F-statistic: 0.04191 on 1 and 86 DF, p-value: 0.8383
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8806 -0.4796 -0.1026 0.2953 2.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.773380 5.067820 -0.547 0.586
## YEAR 0.001855 0.002566 0.723 0.472
##
## Residual standard error: 0.6116 on 86 degrees of freedom
## Multiple R-squared: 0.006035, Adjusted R-squared: -0.005522
## F-statistic: 0.5222 on 1 and 86 DF, p-value: 0.4719
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8059 -0.4904 -0.2181 0.3535 1.8356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.015153 5.398149 -0.929 0.355
## YEAR 0.002893 0.002734 1.058 0.293
##
## Residual standard error: 0.6514 on 86 degrees of freedom
## Multiple R-squared: 0.01286, Adjusted R-squared: 0.001378
## F-statistic: 1.12 on 1 and 86 DF, p-value: 0.2929
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41004 -0.31896 -0.07345 0.20705 1.23717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.6268229 3.1019411 -0.202 0.840
## YEAR 0.0005138 0.0015709 0.327 0.744
##
## Residual standard error: 0.3743 on 86 degrees of freedom
## Multiple R-squared: 0.001242, Adjusted R-squared: -0.01037
## F-statistic: 0.107 on 1 and 86 DF, p-value: 0.7444
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.47023 -0.28094 -0.08435 0.22301 1.12223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.184282 2.996524 -0.729 0.468
## YEAR 0.001316 0.001517 0.867 0.388
##
## Residual standard error: 0.3616 on 86 degrees of freedom
## Multiple R-squared: 0.00867, Adjusted R-squared: -0.002857
## F-statistic: 0.7522 on 1 and 86 DF, p-value: 0.3882Sources to look at: https://onlinelibrary.wiley.com/doi/full/10.1002/eco.1849 http://adsabs.harvard.edu/abs/2016AGUFMGC32C..04S https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.12743 https://www.sciencedirect.com/science/article/pii/S0190052816300323 https://bioone.org/journals/Rangelands/volume-30/issue-3/1551-501X(2008)30[23:CCAEOT]2.0.CO;2/Climate-Change-and-Ecosystems-of-the-Southwestern-United-States/10.2111/1551-501X(2008)30[23:CCAEOT]2.0.CO;2.full https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008GL035075